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In professional team sports, the collection and analysis of athlete-monitoring data are common practice, with the aim of assessing fatigue and subsequent adaptation responses, examining performance potential, and minimizing the risk of injury and/or illness. Athlete-monitoring systems should be underpinned by appropriate data analysis and interpretation, to enable the rapid reporting of simple and scientifically valid feedback. Using the correct scientific and statistical approaches can improve the confidence of decisions made from athlete-monitoring data. However, little research has discussed and proposed an outline of the process involved in the planning, development, analysis, and interpretation of athlete-monitoring systems. This review discusses a range of methods often employed to analyze athlete-monitoring data to facilitate and inform decision-making processes. There is a wide range of analytical methods and tools that practitioners may employ in athlete-monitoring systems, as well as several factors that should be considered when collecting these data, methods of determining meaningful changes, and various data-visualization approaches. Underpinning a successful athlete-monitoring system is the ability of practitioners to communicate and present important information to coaches, ultimately resulting in enhanced athletic performance.

Purpose: To establish the interunit reliability of a range of global positioning system (GPS)-derived movement indicators, to determine the variation between manufacturers, and to investigate the difference between software-derived and raw data. Methods: A range of movement variables were obtained from 27 GPS units from 3 manufacturers (GPSports EVO, 10 Hz, n = 10; STATSports Apex, 10 Hz, n = 10; and Catapult S5, 10 Hz, n = 7) that measured the same team-sport simulation session while positioned on a sled. The interunit reliability was determined using the coefficient of variation (%) and 90% confidence limits, whereas between-manufacturers comparisons and comparisons of software versus raw processed data were established using standardized effect sizes and 90% confidence limits. Results: The interunit reliability for both software and raw processed data ranged from good to poor (coefficient of variation = 0.2%; ±1.5% to 78.2%; ±1.5%), with distance, speed, and maximal speed exhibiting the best reliability. There were substantial differences between manufacturers, particularly for threshold-based acceleration and deceleration variables (effect sizes; ±90% confidence limits: −2.0; ±0.1 to 1.9; ±0.1), and there were substantial differences between data-processing methods for a range of movement indicators. Conclusions: The interunit reliability of most movement indicators was deemed as good regardless of processing method, suggesting that practitioners can have confidence within systems. Standardized data-processing methods are recommended, due to the large differences between data outputs from various manufacturer-derived software.

Purpose: To investigate the influence of daily and exponentially weighted moving training loads on subsequent nighttime sleep. Methods: Sleep of 14 professional rugby league athletes competing in the National Rugby League was recorded using wristwatch actigraphy. Physical demands were quantified using GPS technology, including total distance, high-speed distance, acceleration/deceleration load (SumAccDec; AU), and session rating of perceived exertion (AU). Linear mixed models determined effects of acute (daily) and subacute (3- and 7-d) exponentially weighted moving averages (EWMA) on sleep. Results: Higher daily SumAccDec was associated with increased sleep efficiency (effect-size correlation; ES = 0.15; ±0.09) and sleep duration (ES = 0.12; ±0.09). Greater 3-d EWMA SumAccDec was associated with increased sleep efficiency (ES = 0.14; ±0.09) and an earlier bedtime (ES = 0.14; ±0.09). An increase in 7-d EWMA SumAccDec was associated with heightened sleep efficiency (ES = 0.15; ±0.09) and earlier bedtimes (ES = 0.15; ±0.09). Conclusions: The direction of the associations between training loads and sleep varied, but the strongest relationships showed that higher training loads increased various measures of sleep. Practitioners should be aware of the increased requirement for sleep during intensified training periods, using this information in the planning and implementation of training and individualized recovery modalities.

Purpose: To assess associations between a free oxygen radical test (FORT), free oxygen radical defense test (FORD), oxidative stress index, urinary cortisol, countermovement jump (CMJ), and subjective wellness in American college football. Methods: Twenty-three male student athlete American college football players were assessed over 10 weeks: off-season conditioning (3 wk), preseason camp (4 wk), and in season (3 wk). Assessments included a once-weekly FORT and FORD blood sample, urinary cortisol sample, CMJ assessment including flight time, reactive strength index modified and concentric impulse, and a daily subjective wellness questionnaire. Linear mixed models analyzed the effect of a 2 within-subject SD change in the predictor variable on the dependent variable. The effects were interpreted using magnitude-based inference and are presented as standardized effect size (ES) ± 90% confidence intervals. Results: Small negative associations were observed between FORT–flight time, FORT–fatigue, FORT–soreness (ES range = −0.30 to −0.48), FORD–sleep (ES = 0.42 ± 0.29), and oxidative stress index soreness (ES = 0.56 ± 0.29). Small positive associations were observed between FORT–cortisol (ES = 0.36 ± 0.35), FORD–flight time, FORD reactive strength index modified and FORD–soreness (0.37–0.41), oxidative stress index concentric impulse (ES = 0.37 ± 0.28), and with soreness–concentric impulse, soreness–flight time, and soreness reactive strength index modified (0.33–0.59). Moderate positive associations were observed between cortisol–concentric impulse and cortisol–sleep (0.57–0.60). Conclusion: FORT/FORD was associated with CMJ variables and subjective wellness. Greater amounts of subjective soreness were associated with decreased CMJ performance, increased FORT and cortisol, and decreased FORD.

Purpose: To assess objective strain and subjective muscle soreness in “Bigs” (offensive and defensive line), “Combos” (tight ends, quarterbacks, line backers, and running backs), and “Skills” (wide receivers and defensive backs) in American college football players during off-season, fall camp, and in-season phases. Methods: Twenty-three male players were assessed once weekly (3-wk off-season, 4-wk fall camp, and 3-wk in-season) for hydroperoxides (free oxygen radical test [FORT]), antioxidant capacity (free oxygen radical defense test [FORD]), oxidative stress index (OSI), countermovement-jump flight time, Reactive Strength Index (RSI) modified, and subjective soreness. Linear mixed models analyzed the effect of a 2-within-subject-SD change between predictor and dependent variables. Results: Compared to fall camp and in-season phases, off-season FORT (P ≤ .001 and <.001), FORD (P ≤ .001 and <.001), OSI (P ≤ .001 and <.001), flight time (P ≤ .001 and <.001), RSI modified (P ≤ .001 and <.001), and soreness (P ≤ .001 and <.001) were higher for “Bigs,” whereas FORT (P ≤ .001 and <.001) and OSI (P = .02 and <.001) were lower for “Combos.” FORT was higher for “Bigs” compared to “Combos” in all phases (P ≤ .001, .02, and .01). FORD was higher for “Skills” compared with “Bigs” in off-season (P = .02) and “Combos” in-season (P = .01). OSI was higher for “Bigs” compared with “Combos” (P ≤ .001) and “Skills” (P = .01) during off-season and to “Combos” in-season (P ≤ .001). Flight time was higher for “Skills” in fall camp compared with “Bigs” (P = .04) and to “Combos” in-season (P = .01). RSI modified was higher for “Skills” during off-season compared with “Bigs” (P = .02) and “Combos” during fall camp (P = .03), and in-season (P = .03). Conclusion: Off-season American college football training resulted in higher objective strain and subjective muscle soreness in “Bigs” compared with fall camp and during in-season compared with “Combos” and “Skills” players.

High levels of lean mass are important in collision-based sports for the development of strength and power, which may also assist during contact situations. While skinfold-based measures have been shown to be appropriate for cross-sectional assessments of body composition, their utility in tracking changes in lean mass is less clear.